English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Entity standardization maps noisy mentions from free-form text to standardentities in a knowledge base. The unique challenge of this task relative toother entity-related tasks is the lack of surrounding context and numerousvariations in the surface form of the mentions, especially when it comes togeneralization across domains where labeled data is scarce. Previous researchmostly focuses on developing models either heavily relying on context, ordedicated solely to a specific domain. In contrast, we propose CoSiNES, ageneric and adaptable framework with Contrastive Siamese Network for EntityStandardization that effectively adapts a pretrained language model to capturethe syntax and semantics of the entities in a new domain.  We construct a new dataset in the technology domain, which contains 640technical stack entities and 6,412 mentions collected from industrial contentmanagement systems. We demonstrate that CoSiNES yields higher accuracy andfaster runtime than baselines derived from leading methods in this domain.CoSiNES also achieves competitive performance in four standard datasets fromthe chemistry, medicine, and biomedical domains, demonstrating its cross-domainapplicability.

CoSiNES: Contrastive Siamese Network for Entity Standardization